On-line multivariate statistical monitoring of batch processes using Gaussian mixture model

Computers and Chemical Engineering

Volumn 34, Issue 4, 2010, Pages 500-507

  Chen, Tao ^a   Zhang, Jie ^b  

^a Nanyang Technological University   (Singapore)

^b NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

Batch processes; Fault detection and diagnosis; Mixture model; Multivariate statistical process monitoring; Principal component analysis; Probability density estimation

Indexed keywords

BATCH PROCESS; FAULT DETECTION AND DIAGNOSIS; MIXTURE MODEL; MULTIVARIATE STATISTICAL PROCESS MONITORING; PROBABILITY DENSITY ESTIMATION;

BATCH DATA PROCESSING; COMMUNICATION CHANNELS (INFORMATION THEORY); FAULT DETECTION; IMAGE SEGMENTATION; MAGNETOSTRICTIVE DEVICES; MIXTURES; MULTIVARIANT ANALYSIS; NANOELECTRONICS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; PROCESS MONITORING; STATISTICAL PROCESS CONTROL;

PROBABILITY DENSITY FUNCTION;

EID: 77649189520     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2009.08.007     Document Type: Article

References (26)

1. Berger J.O. Statistical decision theory and Bayesian analysis. 2nd Ed. (1985), Springer
2. Bishop C.M. Pattern recognition and machine learning (2006), Springer
3. Chen J.H., and Liu K.C. On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science 57 (2002) 63-75
4. 2 and Q statistics for process monitoring. Control Engineering Practice 12 (2004) 745-755
5. Chen T., Morris J., and Martin E. Probability density estimation via an infinite Gaussian mixture model: Application to statistical process monitoring. Journal of the Royal Statistical Society C: Applied Statistics 55 (2006) 699-715
6. Choi S.W., Park J.H., and Lee I.-B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Computers and Chemical Engineering 28 (2004) 1377-1387
7. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society B 39 (1977) 1-38
8. Dunia R., Qin S., Edgar T., and McAvoy T. Identification of faulty sensors using PCA. AIChE Journal 42 (1996) 2797-2812
9. Jolliffe I.T. Principal component analysis. 2nd Ed. (2002), Springer
10. Kano M., and Nakagawa Y. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry. Computers and Chemical Engineering 32 (2008) 12-24
11. Lee J.-M., Yoo C., and Lee I.-B. Fault detection of batch processes using multiway kernel principal component analysis. Computers and Chemical Engineering 28 (2004) 1837-1847
12. Lu N., Yao Y., and Gao F. Two-dimensional dynamic PCA for batch process monitoring. AIChE Journal 51 (2005) 3300-3304
13. Martin E.B., and Morris A.J. Non-parametric confidence bounds for process performance monitoring charts. Journal of Process Control 6 (1996) 349-358
14. Martin E.B., Morris A.J., and Kiparrisides C. Manufacturing performance enhancement through multivariate statistical process control. Annual Reviews in Control 23 (1999) 35-44
15. Miller P., Swanson R.E., and Heckler C.F. Contribution plots: A missing link in multivariate quality control. International Journal of Applied Mathematics and Computer Science 8 (1998) 775-792
16. Nomikos P., and MacGregor J.F. Multi-way partial least squares in monitoring batch processes. Chemometrics and Intelligent Laboratory Systems 30 (1995) 97-108
17. Nomikos P., and MacGregor J.F. Multivariate SPC charts for monitoring batch processes. Technometrics 37 (1995) 41-59
18. Qin S.J. Statistical process monitoring: Basics and beyond. Journal of Chemometrics 17 (2003) 480-502
19. Rannar H., MacGregor J.F., and Wold S. Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems 41 (1998) 73-81
20. Safavi A.A., Chen J., and Romagnoli J.A. Wavelet-based density estimation and application to process monitoring. AIChE Journal 43 (1997) 1227-1241
21. Schwarz G. Estimating the dimension of a model. Annals of Statistics 6 (1978) 461-464
22. Thissen U., Swierenga H., de Weijer A.P., Wehrens R., Melssen W.J., and Buydens L.M.C. Multivariate statistical process control using mixture modeling. Journal of Chemometrics 19 (2005) 23-31
23. Wilson D.J.H., Irwin G.W., and Lightbody G. RBF principal manifolds for process monitoring. IEEE Transactions on Neural Networks 10 (1999) 1424-1434
24. Wise B.M., Gallagher N.B., Butler S.W., White D.D., and Barna G.G. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics 13 (1999) 379-396
25. Yu J., and Qin S.J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal 54 (2008) 1811-1829
26. Yue H., and Qin S. Reconstruction based fault identification using a combined index. Industrial and Engineering Chemistry Research 40 (2001) 4403-4414